• The Journal of Information Systems
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• v.31 no.1
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• pp.289-304
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• 2022

Purpose The development of the network and mobile industries has induced companies to invest in information systems, leading a new industrial revolution. The Journal of Information Systems, which developed the information system field into a theoretical and practical study in the 1990s, retains a 30-year history of information systems. This study aims to identify academic values and research trends of JIS by analyzing the trends. Design/methodology/approach This study aims to analyze the trend of JIS by compounding various methods, named as TLS mining analysis. TLS mining analysis consists of a series of analysis including Term Frequency-Inverse Document Frequency (TF-IDF) weight model, Latent Dirichlet Allocation (LDA) topic modeling, and a text mining with Semantic Network Analysis. Firstly, keywords are extracted from the research data using the TF-IDF weight model, and after that, topic modeling is performed using the Latent Dirichlet Allocation (LDA) algorithm to identify issue keywords. Findings The current study used the summery service of the published research paper provided by Korea Citation Index to analyze JIS. 714 papers that were published from 2002 to 2012 were divided into two periods: 2002-2011 and 2012-2021. In the first period (2002-2011), the research trend in the information system field had focused on E-business strategies as most of the companies adopted online business models. In the second period (2012-2021), data-based information technology and new industrial revolution technologies such as artificial intelligence, SNS, and mobile had been the main research issues in the information system field. In addition, keywords for improving the JIS citation index were presented.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• The Journal of the Convergence on Culture Technology
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• v.10 no.5
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• pp.667-671
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• 2024

Professional job training for government officers emphasizes establishing desirable values as public officials and improving professionalism in public service. To provide customized education, some studies are analyzed factors affecting education satisfaction. However, there is a lack of research predicting education satisfaction with educational contents. Therefore, we propose a deep learning-based regression model that predicts government officer education satisfaction with educational contents. We use education information data for government officer. We use one-hot encoding to categorize variables collected in text format, such as education targets, education classifications, and education types. We quantify the education contents stored in text format as TF-IDF. We train our deep learning-based regression model and validate model performance with 10-Fold Cross Validation. Our proposed model showed 99.87% accuracy on test sets. We expect that customized education recommendations based on our model will help provide and improve optimized education content.

• Journal of muscle and joint health
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• v.27 no.3
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• pp.255-267
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• 2020

Purpose: The purpose of this study is to compare topics through keywords related to nurses in internet portals and social media Pre coronavirus disease (COVID-19) era and during the COVID-19 era. Methods: For six months before and during the outbreak of COVID-19 in Korea, "nurse" was searched on the internet. For data collection, we implemented web crawlers in programming languages such as Python and collected keywords. The keywords collected were classified into three domains of topic Modeling. Results: The keyword 'nurse' increased by 15% during COVID-19 era. Keywords that ranked high in Term Frequency - Inverse Document Frequency (TF-IDF) values were before COVID-19, such as "nurse" and "C-section". during COVID-19, however, they were not only "nurse" but also "emergency" and "gown" related to pandemics. Conclusion: Various topics were being uploaded into the internet media. Nursing professionals should be interested in the text that is revealed in the internet media and try to continuously identify and improve problems.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.147-159
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• 2022

Nowadays, various social media platforms are widely spread and people closely use such platforms in daily life. By doing so, social influencers with a large number of subscribers, views, and comments have huge impact in our society. Following this trend, many companies are actively using influencers for marketing purpose to promote their products and services. In this study, we extract the speeches of influencers from videos for game marketing and analyze them using various text mining techniques. In the analysis, we distinguish game videos leading to successful marketing and failed marketing, and we explore and compare the linguistic features of the influencers for successful and failed marketings.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.421-431
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• 2023

Nuclear power plants (NPPs) have technical specifications (Tech Specs) to ensure that the equipment and key operating parameters necessary for the safe operation of the power plant are maintained within limiting conditions for operation (LCO) determined by a safety analysis. The LCO of Tech Specs that identify the lowest functional capability of equipment required for safe operation for a facility must be complied for the safe operation of NPP. There have been previous studies to aid in compliance with LCO relevant to rule-based expert systems; however, there is an obvious limit to expert systems for implementing the rules for many situations related to LCO. Therefore, in this study, we present a retrieval methodology for similar LCO cases in determining whether LCO is met or not met. To reflect the natural language processing of NPP features, a domain dictionary was built, and the optimal term frequency-inverse document frequency variant was selected. The retrieval performance was improved by adding a Boolean retrieval model based on terms related to the LCO in addition to the vector space model. The developed domain dictionary and retrieval methodology are expected to be exceedingly useful in determining whether LCO is met.

• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.123-141
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• 2021

As the dependence of technology on the economic structure increases, the importance of National Core Technology is increasing. However, due to the nature of the technology itself, it is difficult to determine the scope of the technology to be protected because the scope of the relation is abstract and information disclosure is limited due to the nature of the National Core Technology. To solve this problem, we propose the most appropriate literature type and method of analysis to distinguish important technologies related to National Core Technology. We conducted a pilot test to apply TF-IDF, and LDA topic modeling, two techniques of text mining analysis for big data analysis, to four types of literature (news, papers, reports, patents) collected with National Core Technology keywords in the field of Display industry. As a result, applying LDA theme modeling to patent data are highly relevant to National Core Technology. Important technologies related to the front and rear industries of displays, including OLEDs and microLEDs, were identified, and the results were visualized as networks to clarify the scope of important technologies associated with National Core Technology. Throughout this study, we have clarified the ambiguity of the scope of association of technologies and overcome the limited information disclosure characteristics of national core technologies.

• Journal of Practical Agriculture & Fisheries Research
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• v.22 no.1
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• pp.113-129
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• 2020

In this study we examined the topic analysis and correlation analysis by text mining to extract meaningful information or rules from the self introduction letter of freshman at Korea National College of Agriculture and Fisheries in 2020. The analysis items are described in items related to 'academic' and 'in-school activities' during high school. In the text mining results, the keywords of 'academic' items were 'study', 'thought', 'effort', 'problem', 'friend', and the key words of 'in-school activities' were 'activity', 'thought', 'friend', 'club', 'school' in order. As a result of the correlation analysis, the key words of 'thinking', 'studying', 'effort', and 'time' played a central role in the 'academic' item. And the key words of 'in-school activities' were 'thought', 'activity', 'school', 'time', and 'friend'. The results of frequency analysis and association analysis were visualized with word cloud and correlation graphs to make it easier to understand all the results. In the next study, TF-IDF(Term Frequency-Inverse Document Frequency) analysis using 'frequency of keywords' and 'reverse of document frequency' will be performed as a method of extracting key words from a large amount of documents.

• Journal of health informatics and statistics
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• v.43 no.4
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• pp.329-335
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• 2018

Objectives: In recent years, there has been an increased need for a way to extract desired information from multiple medical literatures at once. This study was conducted to confirm the usefulness of unstructured data analysis using previously published medical literatures to search for new indications. Methods: The new indications were searched through text mining, network analysis, and topic modeling analysis using 5,057 articles of atorvastatin, a treatment for hyperlipidemia, from 1990 to 2017. Results: The extracted keywords was 273. In the frequency of text mining and network analysis, the existing indications of atorvastatin were extracted in top level. The novel indications by Term Frequency-Inverse Document Frequency (TF-IDF) were atrial fibrillation, heart failure, breast cancer, rheumatoid arthritis, combined hyperlipidemia, arrhythmias, multiple sclerosis, non-alcoholic fatty liver disease, contrast-induced acute kidney injury and prostate cancer. Conclusions: Unstructured data analysis for discovering new indications from massive medical literature is expected to be used in drug repositioning industries.

• Journal of Platform Technology
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• v.11 no.3
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• pp.12-23
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• 2023

In the 2020s, the K-Pop market has been dominated by girl groups over boy groups and the fourth generation over the third generation. This paper presents methods and results on lyric clustering to investigate whether the generation of girl groups has started to change. We collected meta-information data for 1469 songs of 47 groups released from 2013 to 2022 and classified them into lyric information and non-lyric meta-information and quantified them respectively. The lyrics information was preprocessed by applying word-translation frequency vectorization based on previous studies and then selecting only the top vector values. Non-lyric meta-information was preprocessed and applied with One-Hot Encoding to reduce the bias of using only lyric information and show better clustering results. The clustering performance on the preprocessed data is 129%, 45% higher for Spherical K-Means' Silhouette Score and Calinski-Harabasz Score, respectively, compared to Hierarchical Clustering. This paper is expected to contribute to the study of Korean popular song development and girl group lyrics analysis and clustering.